Abstract
Automated object recognition of images or signals is
important, to identify items of interest, or anomalies
(such as tumours in tissues). In such analyses it is
often necessary to deal with noise in the values
observed. Such noise complicates automated search
procedures, and can affect the solution. In our
example, the location, orientation and dimensions of an
elliptical object are determined based on noisy data
from electromagnetic surveys. We then use a global
optimisation approach to find the best function fit.
Our results demonstrate the success of this general
approach.
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